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1,479
Selection Site
"... *Abstract problems of Location Selection. Although no list can be considered exhaustive, Moore’s [1] enunciation of criteria still appears pertinent and can be considered as a fundamental basis for selection. Table 1 encapsulates some common criteria for selection. This paper uses the well-establish ..."
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*Abstract problems of Location Selection. Although no list can be considered exhaustive, Moore’s [1] enunciation of criteria still appears pertinent and can be considered as a fundamental basis for selection. Table 1 encapsulates some common criteria for selection. This paper uses the well-established
CF-GeNe: Fuzzy Framework for Robust Gene Regulatory Network Inference
"... Abstract — Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper presents an innovative Collateral-Fuzzy Gene Regulatory Network Reconstruction (CF-GeNe) framework for Gene Regulato ..."
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Cited by 1 (0 self)
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Regulatory Network (GRN) inference. The approach uses the Collateral Missing Value Estimation (CMVE) algorithm as its core to estimate missing values in microarray gene expression data. CF-GeNe also mimics the inherent fuzzy nature of gene co-regulation by applying fuzzy clustering principles using the well-established
A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data
- IEEE TRANS. ON MEDICAL IMAGING
, 2002
"... In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the ..."
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Cited by 118 (1 self)
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with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities
Adaptive fuzzy segmentation of magnetic resonance images
- IEEE TRANS. MED. IMAG
, 1999
"... An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-me ..."
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Cited by 158 (10 self)
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An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means
Modified Fuzzy Possibilistic C-means
"... Abstract — Clustering (or cluster analysis) has been used widely in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into clusters, such that items within a cluster are more similar to each other than they are items in the other clusters. A Mod ..."
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Cited by 3 (0 self)
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Modified fuzzy possibilistic clustering algorithm was developed based on the conventional fuzzy possibilistic c-means (FPCM) to obtain better quality clustering results. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM and FPCM methods.
An Adaptive Fuzzy C-Means Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities
- Pattern Recognition Letters
, 1998
"... We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy C-means algorithm to include a multiplier field, whic ..."
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Cited by 65 (6 self)
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We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy C-means algorithm to include a multiplier field
Median Variant of Fuzzy c-Means
"... Abstract. In this paper we introduce Median Fuzzy C-Means (M-FCM). This algorithm extends the Median C-Means (MCM) algorithm by allowing fuzzy values for the cluster assignments. To evaluate the performance of M-FCM, we compare the results with the clustering obtained by employing MCM and Median Neu ..."
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Abstract. In this paper we introduce Median Fuzzy C-Means (M-FCM). This algorithm extends the Median C-Means (MCM) algorithm by allowing fuzzy values for the cluster assignments. To evaluate the performance of M-FCM, we compare the results with the clustering obtained by employing MCM and Median
On Efficiency of Optimization in Fuzzy c-Means
"... The efficiency of optimization in fuzzy c-means clustering is investigated. Numerous, powerful, general-purpose simultaneous optimization (SO) methods, and hybrid methods combining these and the most widely used alternating optimization (AO) method, are extensively tested for speed comparison. AO is ..."
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Cited by 3 (1 self)
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The efficiency of optimization in fuzzy c-means clustering is investigated. Numerous, powerful, general-purpose simultaneous optimization (SO) methods, and hybrid methods combining these and the most widely used alternating optimization (AO) method, are extensively tested for speed comparison. AO
On the selection of m for Fuzzy c-Means
"... Fuzzy c-means is a well known fuzzy clustering al-gorithm. It is an unsupervised clustering algorithm that permits us to build a fuzzy partition from data. The algorithm depends on a parameter m which corresponds to the degree of fuzziness of the solu-tion. Large values of m will blur the classes an ..."
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Fuzzy c-means is a well known fuzzy clustering al-gorithm. It is an unsupervised clustering algorithm that permits us to build a fuzzy partition from data. The algorithm depends on a parameter m which corresponds to the degree of fuzziness of the solu-tion. Large values of m will blur the classes
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